000120121 001__ 120121
000120121 005__ 20240319081025.0
000120121 0247_ $$2doi$$a10.1109/ACCESS.2022.3214994
000120121 0248_ $$2sideral$$a130819
000120121 037__ $$aART-2022-130819
000120121 041__ $$aeng
000120121 100__ $$aFornas, Javier Granado
000120121 245__ $$aDetection and Classification of Fault Types in Distribution Lines by Applying Contrastive Learning to GAN Encoded Time-Series of Pulse Reflectometry Signals
000120121 260__ $$c2022
000120121 5060_ $$aAccess copy available to the general public$$fUnrestricted
000120121 5203_ $$aT This study proposes a new method for detecting and classifying faults in distribution lines. The physical principle of classification is based on time-domain pulse reflectometry (TDR). These
high-frequency pulses are injected into the line, propagate through all of its bifurcations, and are reflected back to the injection point. According to the impedances encountered along the way, these signals carry information regarding the state of the line. In the present work, an initial signal database was obtained using the TDR technique, simulating a real distribution line using (PSCADTM). By transforming these signals into images and reducing their dimensionality, these signals are processed using convolutional neural networks (CNN). In particular, in this study, contrastive learning in Siamese networks was used for the classification of different types of faults (ToF). In addition, to avoid the problem of overfitting owing to the scarcity of examples, generative adversarial neural networks (GAN) have been used to synthesise new examples, enlarging the initial database. The combination of Siamese neural networks and GAN allows the classification of this type of signal using only synthesised examples to train and validate and only the original examples to test the network. This solves the problem of the lack of original examples in this type of signal of natural phenomena which are difficult to obtain and simulate.
000120121 536__ $$9info:eu-repo/grantAgreement/EC/H2020/864579/EU/Interoperable solutions for implementing holistic FLEXIbility services in the distribution GRID/FLEXIGRID$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 864579-FLEXIGRID
000120121 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000120121 590__ $$a3.9$$b2022
000120121 592__ $$a0.926$$b2022
000120121 591__ $$aCOMPUTER SCIENCE, INFORMATION SYSTEMS$$b73 / 158 = 0.462$$c2022$$dQ2$$eT2
000120121 591__ $$aTELECOMMUNICATIONS$$b41 / 88 = 0.466$$c2022$$dQ2$$eT2
000120121 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b100 / 274 = 0.365$$c2022$$dQ2$$eT2
000120121 593__ $$aComputer Science (miscellaneous)$$c2022$$dQ1
000120121 593__ $$aMaterials Science (miscellaneous)$$c2022$$dQ1
000120121 593__ $$aEngineering (miscellaneous)$$c2022$$dQ1
000120121 594__ $$a9.0$$b2022
000120121 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000120121 700__ $$0(orcid)0000-0002-9582-8964$$aJaraba, Elias Herrero$$uUniversidad de Zaragoza
000120121 700__ $$aEstopinan, Andres Llombart
000120121 700__ $$aSaldana, Jose
000120121 7102_ $$15008$$2785$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Tecnología Electrónica
000120121 773__ $$g10 (2022), 110521-110536$$pIEEE Access$$tIEEE Access$$x2169-3536
000120121 8564_ $$s1856755$$uhttps://zaguan.unizar.es/record/120121/files/texto_completo.pdf$$yVersión publicada
000120121 8564_ $$s2542314$$uhttps://zaguan.unizar.es/record/120121/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000120121 909CO $$ooai:zaguan.unizar.es:120121$$particulos$$pdriver
000120121 951__ $$a2024-03-18-16:38:04
000120121 980__ $$aARTICLE